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The purpose is to theorize and empirically estimate the impact of the gendered nature of the offender-victim dyad and crime type on time to arrest.

Predictions regarding the impact of gendered offender-victim dyads and crime type on time to arrest are constructed by extending role congruity theory and tested using data from the FBI's National Incident-Based Reporting System across five crime types using dyadic-based event history methods.

The authors find strong empirical support that role expectations derived from the gender composition of offender-victim dyads and the masculinity of the crime type affect time to clearance.

This research is the first to theorize and empirically test the relative impact of role congruency and the relational nature of the offender-victim dyad in the adjudication process. Furthermore, the research shows that the construction of “normal crime” can be enhanced by applying a gendered and relational approach, based on social psychological theory, which is predictive of crime clearance.

Future research is required to validate the results for crimes where law enforcement has less discretion and are feminine typed.

The results imply that by accounting for the expectations generated by gender roles when applied to offender-victim dyads a casual mechanism is established that better organizes previously inconsistent results with respect to the impact of gender on time to clearance. Thus, the authors' utilization of role congruity theory of gender provides a more consistent explanation for inequalities in time to clearance that may be fruitful for evaluating other steps in the adjudication process.

Detective work is a mainstay of modern law enforcement, but its effectiveness has been much less evaluated than patrol work. To explore what is known about effective investigative practices and to identify evidence gaps, the authors assess the current state of empirical research on investigations.

The authors assess the empirical research about the effectiveness of criminal investigations and detective work in resolving cases and improving clearance rates.

The authors’ analysis of the literature produced 80 studies that focus on seven categories of investigations research, which include the impact that case and situational factors, demographic and neighborhood dynamics, organizational policies and practices, investigative effort, technology, patrol officers and community members have on case resolution. The authors’ assessment shows that evaluation research examining the effectiveness of various investigative activities is rare. However, the broader empirical literature indicates that a combination of organizational policies, investigative effort and certain technologies can be promising in improving investigative outcomes even in cases deemed less solvable.

From an evidence-based perspective, this review emphasizes the need for greater transparency, evaluation and accountability of investigative activities given the resources and importance afforded to criminal investigations.

This review is currently the most up-to-date review of the state of the research on what is known about effective investigative practices.

Purpose – The goal of this chapter is to assess the state of evidence examining and explaining trends and patterns in homicide clearances.

Design/methodology/approach – After reviewing the varying bodies of literature on homicide investigations and clearances, the author assesses the degree of support for the prevailing explanations of why some homicides are more likely to be solved than others. The author also use national data to evaluate several reasons for declining clearances.

Findings – Changes in the nature of homicide and deteriorating police–community relations are likely major contributors to declining clearance rates. The most consistent findings regarding patterns are the greater likelihood of clearance in homicides involving young children, contact weapons, residential locations, and killings not occurring in the course of another crime. Explanations relying on notions of victim devaluing generate the least support. There is considerable support for the legal factors approach and community-level explanations show promise. The findings regarding the role of forensic evidence are mixed. Smaller scale studies are beginning to help identify best practices for homicide investigations.

Originality/value – This chapter assesses several explanations for declining clearance rates and brings together divergent streams of research to summarize the current state of knowledge on homicide clearances, best practices in homicide investigations, and gaps to be filled by further studies.

This paper addresses the clearance of aggravated assaults (AAs). Specifically, the authors consider variations in these clearances over time for large agencies and test which crime, investigation and agency factors are associated with the likelihood of clearance by arrest or exceptional means. In doing this work, they seek to extend the understanding of how police can improve their investigations and ability to solve serious offenses.

Using case, investigative and organizational data collected from seven large police departments selected on the basis of their trajectory of index crime clearances, and measures of case characteristics, investigative effort and organizational best practices, this paper uses descriptive and inferential statistics to analyze AA investigations and case clearance.

Key findings include the following: trajectories of AA clearance vary across large agencies and covary with a measure of organizational best practices, and the relationship between investigative effort and case clearance can depend on organizational practices. The authors find that measures of investigative effort are either not related to case clearance or there is a negative association.

Now that police researchers have a better understanding of AAs and their investigations, they need to test how this knowledge can be used to improve the quality of police investigations. Tests, preferably multi-agency randomized control trials, of new investigative strategies and organizational practices are needed.

This research is original in that it uses a multi-agency sample and crime, investigation and organizational measures to understand AA clearance.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

Proactive planning strategies for “slow-onset” disruptions that affect humanitarian supply chains (SC) developed to address chronic pressing societal problems, can have a significant impact on boosting the operational and financial performance of these chains. The purpose of this paper is to develop a methodology that quantifies the impact of a risk mitigation strategy widely employed in commercial SCs, namely emergency sourcing (ES), on the performance of humanitarian SCs taking into account backorders’ clearance time, unsatisfied demand, and cost.

Discrete event simulation is employed in order to evaluate alternative ES strategies based on a total cost criterion, which incorporates inventory-related costs, as well as premium contract costs paid for emergency replenishment. Backorders’ clearance time and time-to-recovery are also employed as a design parameters.

The results document the significant impact of disruptions on expected total cost, and the beneficial role of ES in hedging against disruptions. To that end, the proposed methodology determines the optimal emergency contracted capacity for a given premium, or alternatively the maximum premium cost value that ensures the feasibility of the implemented ES strategy in the long-run, along with the associated cost and time savings, and reduction of the unsatisfied demand.

The fundamental objective is to provide a decision-making support methodology for deciding on whether to implement an ES strategy or not in humanitarian SCs, and the level of the optimal contracted reserved capacity. The results could be of great value to aid providers, policy-makers, and regulators.

Clearance rates for nonfatal shootings, especially cases involving gang- and drug-related violence, are disturbingly low in many US cities. Using data from a previously completed project in Boston, we explore the prospects for improving gang/drug nonfatal shooting cases by investing the same investigative effort found in similar gang/drug gun murder cases.

Our analyses primarily focus on a sample of 231 nonfatal shootings that occurred in Boston from 2010 to 2014. Logistic regressions are first used to analyze differences in the likelihood of case clearance for gang/drug nonfatal shooting cases relative to other nonfatal shooting cases. Independent samples t-tests are then used to compare the investigative characteristics of these two different kinds of nonfatal shootings. Next, independent samples t-tests are used to compare the investigation of gang/drug gun assaults relative to the investigation of very similar gang/drug gun homicides.

Results demonstrate that the odds of clearing gang/drug nonfatal shootings are 77.2% less likely relative to the odds of clearing nonfatal shootings resulting from other circumstances. This stark difference in clearance rates is not driven by diminished investigative effort, but investigative effort does matter. Relative to gang/drug gun assaults, gang/drug gun homicides have much higher clearance rates that are the result of greater investigative resources and effort that produces significantly more witnesses and evidence, and generate more forensic tests and follow-up investigative actions.

Gang- and drug-related violence generates a bulk of urban nonfatal shootings. Low clearance rates for nonfatal shootings undermine police efforts to hold offenders accountable, disrupt cycles of gun violence, and provide justice to victims. Police should make investments to improve investigative effort such as handling these cases with the same vigor as homicide cases.

The purpose of this paper is to improve the efficiency of accident management from the angle of reducing highway accident response times while taking into account total daily work hours.

The authors developed a patrol beat scheduling model, which is formulated as a chance-constrained optimization model, with the objective of minimizing the sum of officer work hours. Along with the model, a simulation program was also developed to help evaluate the effectiveness of the model-generated beat schedule in terms of response times.

This study concluded that, first, the current manually designed beat schedule could be improved should the National Highway Police Bureau adopt the proposed model. Second, the total daily work hours of the model-generated schedule at the confidence level of 100 percent were 64 hours, 21 hours less than the average work hours recorded in the 2006 data, or about an improvement of 24 percent. Should the model be adopted, not only response times will be improved, the 24 percent reduction in work hours could be translated into a cut in personnel cost.

The scheduling model and simulation program are both built upon one-year historical data whose accuracy and completeness is prerequisite.

The proposed model can be adopted by other public service agencies such as fire departments, or emergency service centers. By replacing the historical data used in the study with their own data, agencies can evaluate the efficiency of their existing schedule or generate various schedules based on institutional needs.

This model utilizes historical accident data to generate optimal beat schedule and evaluate the efficiency of such schedule. Similar models have not been found in other studies.

As China’s e-commerce and cross-border e-commerce rapidly develop, the cross-border e-commerce supply chain exhibits characteristics of globalized development scale, collaborative multiparty participation, streamlined management processes, digitalized production and trade and flexible strategic choices. It tends toward data-driven intelligence, interoperable information collaboration, personalized order responses, sustainable supply chain management and secure blockchain technology. These characteristics and trends provide critical references for businesses, governments and investors.

In response to issues such as inconsistent legal regulations, imbalanced logistics and transportation, imperfect payment settlements and opaque supply chains.

It is recommended to take measures to strengthen cooperation and communication, optimize logistics, reduce customs clearance difficulties, reinforce safeguard measures and promote sustainable development, collectively fostering the healthy growth of cross-border e-commerce.

With the rapid development of cross-border e-commerce, green and low-carbon initiatives have become a significant trend in this sector. The cross-border e-commerce supply chain refers to the mechanism that reduces environmental impacts and enhances resource efficiency from manufacturers to consumers. It primarily involves manufacturers, e-commerce platforms, logistics companies and payment and settlement processes. The cross-border e-commerce supply chain is gradually becoming a highlight in China’s foreign trade, supporting the concept of “buying globally and selling globally” and connecting the “world’s factory” with the “world’s market.”

Surveys of the public have been conducted to document and explain evacuation behaviour in a wide range of threatening events during the past half-century. Many of the behaviours are directly applicable to transportation modelling and management: whether people evacuate, when they depart, where they go, the routes they employ and the number of vehicles they use. Data have usually been collected by telephone interview or mailed questionnaires. Traditional survey methods should be supplemented by Internet surveys, traffic counts and GPS tracking. More real-time data collection should be employed to document a wider range of behaviours during a threat more accurately and to better understand the dynamics of evacuation decisions.